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| import clip | |
| import torch | |
| import gradio as gr | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| try: | |
| from torchvision.transforms import InterpolationMode | |
| BICUBIC = InterpolationMode.BICUBIC | |
| except ImportError: | |
| BICUBIC = Image.BICUBIC | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| #MODEL_PATH = '/media/delta/S/clipmodel.pth' #CHANGE THIS IF YOU WANT TO CHANGE THE MODEL PATH | |
| MODEL_PATH ='/media/delta/S/clipmodel_large.pth' #CHANGE THIS IF YOU WANT TO CHANGE THE MODEL PATH | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = clip.model.build_model(torch.load(MODEL_PATH)).to(device) | |
| preprocess = clip.clip._transform(model.visual.input_resolution) | |
| def zeroshot_detection(Press_Clear_Dont_Stack_Image): | |
| inp = Press_Clear_Dont_Stack_Image | |
| captions = "photo of a guardrail, no guardrail in the photo" #CHANGE THIS IF YOU WANT TO CHANGE THE PREDICTION: separate by commas | |
| captions = captions.split(',') | |
| caption = clip.tokenize(captions).to(device) | |
| image = preprocess(inp).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| image_features = model.encode_image(image) | |
| text_features = model.encode_text(caption) | |
| image_features /= image_features.norm(dim=-1, keepdim=True) | |
| text_features /= text_features.norm(dim=-1, keepdim=True) | |
| similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1) | |
| values, indices = similarity[0].topk(len(captions)) | |
| return {captions[indices[i].item()]: float(values[i].item()) for i in range(len(values))} | |
| gr.Interface(fn=zeroshot_detection, | |
| inputs=[gr.Image(type="pil")], | |
| outputs=gr.Label(num_top_classes=1)).launch() | |